Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement for Multiplexed Images.
Ontology highlight
ABSTRACT:
SUBMITTER: Bai Y
PROVIDER: S-EPMC8289709 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Publications
Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement for Multiplexed Images.
Bai Yunhao Y Zhu Bokai B Rovira-Clave Xavier X Chen Han H Markovic Maxim M Chan Chi Ngai CN Su Tung-Hung TH McIlwain David R DR Estes Jacob D JD Keren Leeat L Nolan Garry P GP Jiang Sizun S
Frontiers in immunology 20210705
Multiplex imaging technologies are now routinely capable of measuring more than 40 antibody-labeled parameters in single cells. However, lateral spillage of signals in densely packed tissues presents an obstacle to the assignment of high-dimensional spatial features to individual cells for accurate cell-type annotation. We devised a method to correct for lateral spillage of cell surface markers between adjacent cells termed REinforcement Dynamic Spillover EliminAtion (REDSEA). The use of REDSEA ...[more]
Similar Datasets
Project description:Signal intensity measured in a mass cytometry (CyTOF) channel can often be affected by the neighboring channels due to technological limitations. Such signal artifacts are known as spillover effects and can substantially limit the accuracy of cell population clustering. Current approaches reduce these effects by using additional beads for normalization purposes known as single-stained controls. While effective in compensating for spillover effects, incorporating single-stained controls can be costly and require customized panel design. This is especially evident when executing large-scale immune profiling studies. We present a novel statistical method, named CytoSpill that independently quantifies and compensates the spillover effects in CyTOF data without requiring the use of single-stained controls. Our method utilizes knowledge-guided modeling and statistical techniques, such as finite mixture modeling and sequential quadratic programming, to achieve optimal error correction. We evaluated our method using five publicly available CyTOF datasets obtained from human peripheral blood mononuclear cells (PBMCs), C57BL/6J mouse bone marrow, healthy human bone marrow, chronic lymphocytic leukemia patient, and healthy human cord blood samples. In the PBMCs with known ground truth, our method achieved comparable results to experiments that incorporated single-stained controls. In datasets without ground-truth, our method not only reduced spillover on likely affected markers, but also led to the discovery of potentially novel subpopulations expressing functionally meaningful, cluster-specific markers. CytoSpill (developed in R) will greatly enhance the execution of large-scale cellular profiling of tumor immune microenvironment, development of novel immunotherapy, and the discovery of immune-specific biomarkers. The implementation of our method can be found at https://github.com/KChen-lab/CytoSpill.git.
| S-EPMC8214634 | biostudies-literature
Project description:The advent of mass cytometry increased the number of parameters measured at the single-cell level while decreasing the extent of crosstalk between channels relative to dye-based flow cytometry. Although reduced, spillover still exists in mass cytometry data, and minimizing its effect requires considerable expert knowledge and substantial experimental effort. Here, we describe a novel bead-based compensation workflow and R-based software that estimates and corrects for interference between channels. We performed an in-depth characterization of the spillover properties in mass cytometry, including limitations defined by the linear range of the mass cytometer and the reproducibility of the spillover over time and across machines. We demonstrated the utility of our method in suspension and imaging mass cytometry. To conclude, our approach greatly simplifies the development of new antibody panels, increases flexibility for antibody-metal pairing, opens the way to using less pure isotopes, and improves overall data quality, thereby reducing the risk of reporting cell phenotype artifacts.
| S-EPMC5981006 | biostudies-literature
Project description:Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell type annotation for images with a wide range of antibody panels, without requiring additional model training or human intervention. Our tool has successfully annotated over 3 million cells, revealing the spatial organization of various cell types across more than 40 different human tissues. It is open-source and features a modular design, allowing for easy extension to additional cell types.
| S-EPMC11429614 | biostudies-literature
Project description:MotivationMultiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data.ResultsTo address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation.Availability and implementationThe R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.
| S-EPMC10541135 | biostudies-literature
Project description:Recurrent and new tumors, attributed in part to lateral invasion, are frequent in squamous cell carcinomas and lead to poor survival. We identified a mechanism by which cancer subverts adjacent histologically normal epithelium to enable small clusters of cancer cells to burrow undetected under adjacent histologically normal epithelium. We show that suppression of DMBT1 within cancer promotes aggressive invasion and metastasis in vivo and is associated with metastasis in patients. Cancer cells via TGFβ1 and TNFα also suppress DMBT1 in adjacent histologically normal epithelium, thereby subverting it to promote invasion of a small population of tumor cells. The sufficiency of DMBT1 in this process is demonstrated by significantly higher satellite tumor nests in Dmbt1-/- compared with wild-type mice. Moreover, in patients, invasion of small tumor nests under adjacent histologically normal epithelium is associated with increased risk for recurrence and shorter disease-free survival. This study demonstrates a crucial role of adjacent histologically normal epithelium in invasion and its important role in the tumor microenvironment and opens new possibilities for therapeutic strategies that reduce tumor recurrence.
| S-EPMC8042603 | biostudies-literature
Project description:Land-use intensification is a central element in proposed strategies to address global food security. One rationale for accepting the negative consequences of land-use intensification for farmland biodiversity is that it could 'spare' further expansion of agriculture into remaining natural habitats. However, in many regions of the world the only natural habitats that can be spared are fragments within landscapes dominated by agriculture. Therefore, land-sparing arguments hinge on land-use intensification having low spillover effects into adjacent protected areas, otherwise net conservation gains will diminish with increasing intensification. We test, for the first time, whether the degree of spillover from farmland into adjacent natural habitats scales in magnitude with increasing land-use intensity. We identified a continuous land-use intensity gradient across pastoral farming systems in New Zealand (based on 13 components of farmer input and soil biogeochemistry variables), and measured cumulative off-site spillover effects of fertilisers and livestock on soil biogeochemistry in 21 adjacent forest remnants. Ten of 11 measured soil properties differed significantly between remnants and intact-forest reference sites, for both fenced and unfenced remnants, at both edge and interior. For seven variables, the magnitude of effects scaled significantly with magnitude of surrounding land-use intensity, through complex interactions with fencing and edge effects. In particular, total C, total N, δ15N, total P and heavy-metal contaminants of phosphate fertilizers (Cd and U) increased significantly within remnants in response to increasing land-use intensity, and these effects were exacerbated in unfenced relative to fenced remnants. This suggests movement of livestock into surrounding natural habitats is a significant component of agricultural spillover, but pervasive changes in soil biogeochemistry still occur through nutrient spillover channels alone, even in fenced remnants set aside for conservation. These results have important implications for the viability of land-sparing as a strategy for balancing landscape-level conservation and production goals in agricultural landscapes.
| S-EPMC4289067 | biostudies-literature
Project description:MotivationSingle cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks a unified approach to evaluating segmentation accuracy.ResultsIn this article, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations.Availability and implementationSource code is available on Github at: https://github.com/Roth-Lab/ESQmodel.
| S-EPMC10783950 | biostudies-literature
Project description:MotivationImaging-based spatial transcriptomics has the power to reveal patterns of single-cell gene expression by detecting mRNA transcripts as individually resolved spots in multiplexed images. However, molecular quantification has been severely limited by the computational challenges of segmenting poorly outlined, overlapping cells and of overcoming technical noise; the majority of transcripts are routinely discarded because they fall outside the segmentation boundaries. This lost information leads to less accurate gene count matrices and weakens downstream analyses, such as cell type or gene program identification.ResultsHere, we present Sparcle, a probabilistic model that reassigns transcripts to cells based on gene covariation patterns and incorporates spatial features such as distance to nucleus. We demonstrate its utility on both multiplexed error-robust fluorescence in situ hybridization, single-molecule FISH data, probabilistic cell typing in situ sequencing, spatially resolved transcript amplicon readout mapping and MERFISH from Vizgen. Sparcle improves transcript assignment, providing more realistic per-cell quantification of each gene, better delineation of cell boundaries and improved cluster assignments. Critically, our approach does not require an accurate segmentation and is agnostic to technological platform.Availability and implementationThe code is available at: https://github.com/sandhya212/Sparcle_for_spot_reassignments.Contactsandhya.prabhakaran@moffitt.org.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
| S-EPMC9710569 | biostudies-literature
Project description:The rapid advancement of high-content, single-cell technologies like robotic confocal microscopy with multiplexed dyes (morphological profiling) can be leveraged to reveal fundamental biology, ranging from microbial and abiotic stress to organ development. Specifically, heterogeneous cell systems can be perturbed genetically or with chemical treatments to allow for inference of causal mechanisms. An exciting strategy to navigate the high-dimensional space of possible perturbation and cell type combinations is to use generative models as priors to anticipate high-content outcomes in order to design informative experiments. Towards this goal, we present the Latent diffUsion for Multiplexed Images of Cells (LUMIC) framework that can generate high quality and high fidelity images of cells. LUMIC combines diffusion models with DINO (self-Distillation with NO labels), a vision-transformer based, self-supervised method that can be trained on images to learn feature embeddings, and HGraph2Graph, a hierarchical graph encoder-decoder to represent chemicals. To demonstrate the ability of LUMIC to generalize across cell lines and treatments, we apply it to a dataset of ~27,000 images of two cell lines treated with 306 chemicals and stained with three dyes from the JUMP Pilot dataset and a newly-generated dataset of ~3,000 images of five cell lines treated with 61 chemicals and stained with three dyes. To quantify prediction quality, we evaluate the DINO embeddings, Kernel Inception Distance (KID) score, and recovery of morphological feature distributions. LUMIC significantly outperforms previous methods and generates realistic out-of-sample images of cells across unseen compounds and cell types.
| S-EPMC11581016 | biostudies-literature
Project description:Phenotyping immune cells and cell clusters in situ, including their activation state and function, can aid in interpretation of spatial relationships within the tissue microenvironment. Immune cell phenotypes require multiple biomarkers. However, conventional microscopy setups can only image up to four biomarkers at one time. In this report, we describe and give an example of a workflow to phenotype, quantitate, and visualize greater than four biomarkers in silico utilizing multiplexed fluorescence histology and the TissueFAXS quantitative imaging system with a conventional microscopy setup. Biomarkers were conjugated to Cy3 or Cy5. Multiplexed staining was performed on formalin-fixed paraffin-embedded tissue sections. We imaged the slides, inactivated the dyes, and repeated the process until all biomarkers were stained. Phenotype profiles were built based on in silico combinations of the biomarkers. We used algorithms that aligned all images to create a composite image, isolated each cell in the image, and identified biomarker positive cells in the image. The in silico phenotypes were quantitated and displayed through flow cytometry-like histograms and dot scatterplots in addition to backgating into the tissue images. The advantage of our workflow is that it provides visual verification of cell isolation and identification as well as highlight characteristics of cells and cell clusters. © 2018 International Society for Advancement of Cytometry.
| S-EPMC6500592 | biostudies-literature